Optimizing Bitcoin Price Predictions Using Long Short-Term Memory Algorithm: A Deep Learning Approach

Ali Khumaidi(1*); Panji Kusmanto(2); Nur Hikmah(3);

(1) Universitas Krisnadwipayana
(2) Universitas Krisnadwipayana
(3) Universitas Krisnadwipayana
(*) Corresponding Author



Currently bitcoin is considered an investment tools, the value of bitcoin itself is unstable so it is difficult to predict which can cause losses for bitcoin traders. Some previous research shows that Long Short-Term Memory (LSTM) which is a deep learning approach as an improvement of RNN has the best performance in predicting stocks and cryptocurrencies compared to Support Vector Machine (SVM), Exponential Moving Average (EMA), and Moving Average (MA), and Seasonal Autoregressive Integrated Moving Average (SARIMA). LSTM has the disadvantage that it is difficult to understand in determining the best parameters and to obtain good results it needs strict hyperparameter adjustment. This study aims to find the best parameters in LSTM by selecting the amount of data, training data composition, batch size, epoch and the amount of prediction time and analyzing prediction performance. In this study, data collection was carried out in real time and was able to provide predictions for the next few days. The test results of the LSTM algorithm have a performance with an average accuracy of 93.69% with the parameters of the amount of bitcoin price data used is 3 years, with a percentage of train data of 85%, using 10 batch sizes, with a number of epochs 125, and the highest average accuracy rate for 7 days of prediction.


Bitcoin, Hyperparameter Tuning, LSTM, Prediction, Real Time Data.


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doi  https://doi.org/10.33096/ilkom.v16i1.1831.38-45



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